'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel





@require_sentencepiece
@require_tokenizers
@require_torch
class 							SCREAMING_SNAKE_CASE (					unittest.TestCase			):
  """simple docstring"""







  @slow
  def 	a (	self :       Tuple       ):


        """simple docstring"""



        _lowerCAmelCase		 = XLMRobertaModel.from_pretrained('xlm-roberta-base'       )
        _lowerCAmelCase		 = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]       )
        # The dog is cute and lives in the garden house

        _lowerCAmelCase		 = torch.Size((1, 12, 768)       )  # batch_size, sequence_length, embedding_vector_dim
        _lowerCAmelCase		 = torch.tensor(
            [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]       )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              _lowerCAmelCase		 = model(__lowerCAmelCase       )['last_hidden_state'].detach()
        self.assertEqual(output.shape       ,       __lowerCAmelCase       )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]       ,       __lowerCAmelCase       ,       atol=1e-3       )       )



  @slow
  def 	a (	self :       Union[str, Any]       ):


        """simple docstring"""



        _lowerCAmelCase		 = XLMRobertaModel.from_pretrained('xlm-roberta-large'       )
        _lowerCAmelCase		 = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]       )
        # The dog is cute and lives in the garden house

        _lowerCAmelCase		 = torch.Size((1, 12, 1024)       )  # batch_size, sequence_length, embedding_vector_dim
        _lowerCAmelCase		 = torch.tensor(
            [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]       )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              _lowerCAmelCase		 = model(__lowerCAmelCase       )['last_hidden_state'].detach()
        self.assertEqual(output.shape       ,       __lowerCAmelCase       )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]       ,       __lowerCAmelCase       ,       atol=1e-3       )       )













"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE   =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AdaptiveEmbedding',
		    'TransfoXLForSequenceClassification',
		    'TransfoXLLMHeadModel',
		    'TransfoXLModel',
		    'TransfoXLPreTrainedModel',
		    'load_tf_weights_in_transfo_xl',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAdaptiveEmbedding',
		    'TFTransfoXLForSequenceClassification',
		    'TFTransfoXLLMHeadModel',
		    'TFTransfoXLMainLayer',
		    'TFTransfoXLModel',
		    'TFTransfoXLPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
		from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_transfo_xl import (
				    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AdaptiveEmbedding,
				    TransfoXLForSequenceClassification,
				    TransfoXLLMHeadModel,
				    TransfoXLModel,
				    TransfoXLPreTrainedModel,
				    load_tf_weights_in_transfo_xl,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_transfo_xl import (
				    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAdaptiveEmbedding,
				    TFTransfoXLForSequenceClassification,
				    TFTransfoXLLMHeadModel,
				    TFTransfoXLMainLayer,
				    TFTransfoXLModel,
				    TFTransfoXLPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)